Machine vision is a term that can be used to describe hardware and software technology utilized in recognition of certain patterns and/or objects. Many machine vision systems have been designed and built to address needs for recognition without the need for human interaction. For example, character recognition systems and software can extract and convert printed text to a digital format. Other machine vision systems have been developed to automatically inspect industrial products for defects. Yet other machine vision systems have been developed to recognize human faces.
Various approaches have been applied to improve the classification accuracy for object recognition using machine vision. For example, certain recognition systems perform an analysis of patterns and features of an image for classification. Certain systems may process an image to remove noise and/or to delineate pattern features to enable characteristic measurements. Such systems can utilize a training phase and a recognition phase. During the training phase, information representative of distinctions among pattern types is gathered from a set of training samples of patterns whose proper classification is known (i.e. training set) and stored in a computer's memory in the form of numerical parameters and data structures. During this phase, the selected pattern recognition system is tuned to the training set. Once the training set has been completed, an input image can be processed using the trained numerical parameters and data structures to produce a classification representative of a probability that the input image matches one or more of the training sample.
Certain previous recognition methods work well and can achieve high classification accuracy provided the object is well-lit, and the image is not obscured or corrupted to any significant degree. However, object recognition applications that use a camera on a user's mobile computing device can be particularly challenging due to various factors such as poor lighting, poor contrast, blurring, etc. Applications such as these can suffer from a significant degradation in classification accuracy.
Accordingly, there is a need for improved classification accuracy in object recognition systems.